app.py

import streamlit as st
import joblib
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from tensorflow.keras.preprocessing import image
import os
from PIL import Image

# Load the pre-trained KNN model and class names
knn = joblib.load('knn_model.pk1')
class_names = joblib.load('class_names.pk1')

# Title of the app
st.title("Animal Classification Using KNN Model")

# Description
st.write("Upload an image of an animal and the model will predict which animal it is.")

# Upload image
uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

if uploaded_image is not None:
    # Display image
    img = Image.open(uploaded_image)
    st.image(img, caption='Uploaded Image.', use_column_width=True)

    # Preprocess the image for prediction
    img = img.resize((64, 64))  # Resize the image to match the model's expected size (adjust if needed)
    img_array = np.array(img)  # Convert the image to numpy array
    img_array = img_array.flatten().reshape(1, -1)  # Flatten the image and reshape it to match the input for KNN model

    # Make prediction
    prediction = knn.predict(img_array)
    predicted_class = class_names[prediction[0]]

    # Display prediction
    st.write(f"Prediction: {predicted_class}")